UNIT -1

DATA ENGINEERING
 Data Engineering is the process of organizing, managing, and analyzing large
amounts of data. It's a key component in the world of data science, but it can be used
by anyone who has to deal with big data regularly.
 Data engineering is about collecting, storing, and processing data. It involves
everything from planning to keep your data for long-term use (so you don't lose it) to
finding ways to ensure your servers can handle all the new information you're
collecting.
 The data engineering process covers a sequence of tasks that turn a large amount of
raw data into a practical product meeting the needs of analysts, data scientists,
machine learning engineers, and others. Typically, the end-to-end workflow consists
of the following stages.

A data pipeline is a method in which raw data is ingested from various data sources,
transformed and then ported to a data store, such as a data lake or data warehouse, for
analysis.

Before data flows into a data repository, it usually undergoes some data processing. This is
inclusive of data transformations, such as filtering, masking, and aggregations, which ensure
appropriate data integration and standardization
DATA PIPELINE ARCHITECTURE

Three core steps make up the architecture of a data pipeline.

1. Data ingestion: Data is collected from various sources—including software-as-a-service

(SaaS) platforms, internet-of-things (IoT) devices and mobile devices—and various data
structures, both structured and unstructured data. Within streaming data, these raw data
sources are typically known as producers, publishers, or senders. While businesses can
choose to extract data only when ready to process it, it’s a better practice to land the raw data
within a cloud data warehouse provider first. This way, the business can update any historical
data if they need to make adjustments to data processing jobs. During this data ingestion
process, various validations and checks can be performed to ensure the consistency and
accuracy of data.

2. Data transformation: During this step, a series of jobs are executed to process data into the
format required by the destination data repository. These jobs embed automation and
governance for repetitive workstreams, such as business reporting, ensuring that data is
cleansed and transformed consistently. For example, a data stream may come in a nested
JSON format, and the data transformation stage will aim to unroll that JSON to extract the
key fields for analysis.

3. Data storage: The transformed data is then stored within a data repository, where it can be
exposed to various stakeholders. Within streaming data, this transformed data are typically
known as consumers, subscribers, or recipients.

WHAT IS A DATA PIPELINE?

A data pipeline is a method in which raw data is ingested from various data sources,
transformed and then ported to a data store, such as a data lake or data warehouse, for
analysis.
Before data flows into a data repository, it usually undergoes some data processing. This is
inclusive of data transformations, such as filtering, masking, and aggregations, which ensure
appropriate data integration and standardization. This is particularly important when the
destination for the dataset is a relational database. This type of data repository has a defined
schema which requires alignment—that is, matching data columns and types—to update
existing data with new data.

As the name suggests, data pipelines act as the “piping” for data science projects or business
intelligence dashboards. Data can be sourced through a wide variety of places—APIs, SQL
and NoSQL databases, files, et cetera—but unfortunately, that data usually isn’t ready for
immediate use. During sourcing, data lineage is tracked to document the relationship between
enterprise data in various business and IT applications, for example, where data is currently
and how it’s stored in an environment, such as on-premises, in a data lake or in a data
warehouse.

Data preparation tasks usually fall on the shoulders of data scientists or data engineers, who
structure the data to meet the needs of the business use cases and handle huge amounts of
data. The type of data processing that a data pipeline requires is usually determined through a
mix of exploratory data analysis and defined business requirements. Once the data has been
appropriately filtered, merged, and summarized, it can then be stored and surfaced for use.
Well-organized data pipelines provide the foundation for a range of data projects; this can
include exploratory data analyses, data visualizations, and machine learning tasks.

TYPES OF DATA PIPELINES

There are several main types of data pipelines, each appropriate for specific tasks on specific
platforms.

Batch processing

 The development of batch processing was a critical step in building data

infrastructures that were reliable and scalable. In 2004, MapReduce, a batch
 processing algorithm, was patented and then subsequently integrated into open-source
systems, such as Hadoop, CouchDB and MongoDB.
 As the name implies, batch processing loads “batches” of data into a repository during
set time intervals, which are typically scheduled during off-peak business hours.
 This way, other workloads aren’t impacted as batch processing jobs tend to work with
large volumes of data, which can tax the overall system.
 Batch processing is usually the optimal data pipeline when there isn’t an immediate
need to analyze a specific dataset (for example, monthly accounting), and it is more
associated with the ETL data integration process, which stands for “extract, transform,
and load.”
 Batch processing jobs form a workflow of sequenced commands, where the output of
one command becomes the input of the next command.
 For example, one command might kick off data ingestion, the next command may
trigger filtering of specific columns, and the subsequent command may handle
aggregation.
 This series of commands will continue until the data quality is completely
transformed and rewritten into a data repository.

Streaming data

 Unlike batching processing, streaming data pipelines—also known as event-driven

architectures—continuously process events generated by various sources, such as
sensors or user interactions within an application. Events are processed and analyzed,
and then either stored in databases or sent downstream for further analysis.
 Streaming data is leveraged when it is required for data to be continuously updated.
 For example, apps or point-of-sale systems need real-time data to update inventory
and sales history of their products; that way, sellers can inform consumers if a product
is in stock or not. A single action, such as a product sale, is considered an “event,”
and related events, such as adding an item to checkout, are typically grouped together
as a “topic” or “stream.” These events are then transported via messaging systems or
message brokers, such as the open-source offering, Apache Kafka.
 Since data events are processed shortly after occurring, streaming processing systems
have lower latency than batch systems, but aren’t considered as reliable as batch
 processing systems as messages can be unintentionally dropped or spend a long time
in queue.
 Message brokers help to address this concern through acknowledgements, where a
consumer confirms processing of the message to the broker to remove it from the
queue.

Data integration pipelines

 Data integration pipelines concentrate on merging data from multiple sources into a
single unified view.
 These pipelines often involve extract, transform and load (ETL) processes that clean,
enrich, or otherwise modify raw data before storing it in a centralized repository such
as a data warehouse or data lake.
 Data integration pipelines are essential for handling disparate systems that generate
incompatible formats or structures.
 For example, a connection can be added to Amazon S3 (Amazon Simple Storage
Service)—a service that is offered by Amazon Web Services (AWS) that provides
object storage through a web service interface.

Cloud-native data pipelines

 A modern data platform includes a suite of cloud-first, cloud-native software products

that enable the collection, cleansing, transformation and analysis of an organization’s
data to help improve decision making.
 Today’s data pipelines have become increasingly complex and important for data
analytics and making data-driven decisions.
 A modern data platform builds trust in this data by ingesting, storing, processing and
transforming it in a way that ensures accurate and timely information, reduces data
silos, enables self-service and improves data quality.
DATA PIPELINE VS. ETL PIPELINE

You might find that some terms, such as data pipeline and ETL pipeline, are used
interchangeably in conversation. However, you should think about an ETL pipeline as a
subcategory of data pipelines.

The two types of pipelines are distinguished by three key features:

ETL pipelines follow a specific sequence. As the abbreviation implies, they extract data,
transform data, and then load and store data in a data repository. Not all data pipelines need
to follow this sequence.

In fact, ELT (extract, load, transform) pipelines have become more popular with the advent
of cloud-native tools where data can be generated and stored across multiple sources and
platforms.

While data ingestion still occurs first with this type of pipeline, any transformations are
applied after the data has been loaded into the cloud-based data warehouse.

ETL pipelines also tend to imply the use of batch processing, but as noted above, the scope of
data pipelines is broader. They can also be inclusive of stream processing.

Finally, while unlikely, data pipelines as a whole do not necessarily need to undergo data
transformations, as with ETL pipelines.

It’s rare to see a data pipeline that doesn’t utilize transformations to facilitate data analysis.

Both data pipelines and ETL are responsible for transferring data between sources and
storage solutions, but they do so in different ways.

Data pipelines work with ongoing data streams in real time, while ETL focuses more on
individual “batches” of data for more specific purposes.
DATA ENGINEERING PIPELINE

 A data pipeline combines tools and operations that move data from one system to
another for storage and further handling.
 Constructing and maintaining data pipelines is the core responsibility of data
engineers.
 Among other things, they write scripts to automate repetitive tasks – jobs.
 Commonly, pipelines are used for data migration between systems or environments
(from on-premises to cloud databases);data wrangling or converting raw data into a
usable format for analytics, BI, and machine learning projects; data integration from
various systems and IoT devices; andcopying tables from one database to another.

ETL PIPELINE

 ETL (Extract, Transform, Load) pipeline is the most common architecture that has
been here for decades. It’s often implemented by a dedicated specialist — an ETL
developer.
 As the name suggests, an ETL pipeline automates the following processes.
 Extract — retrieving data. At the start of the pipeline, we’re dealing with raw data
from numerous sources — databases, APIs, files, etc.
 Transform — standardizing data. Having data extracted, scripts transform it to meet
the format requirements. Data transformation significantly improves data
discoverability and usability.
 Load — saving data to a new destination. After bringing data into a usable state,
engineers can load it to the destination, typically a database management system
(DBMS) or data warehouse.

ETL OPERATIONS.
Once the data is transformed and loaded into a centralized repository, it can be used for
further analysis and business intelligence operations, i.e., generating reports, creating
visualizations, etc. The specialist implementing ETL pipelines

ELT PIPELINE

An ELT pipeline performs the same steps but in a different order — Extract, Load,
Transform. Instead of transforming all the collected data, you place it into a data warehouse,
data lake, or data lakehouse. Later, you can process and format it fully or partially, once or
numerous times.

ELT OPERATIONS.

ELT pipelines are preferable when you want to ingest as much data as possible and transform
it later, depending on the needs arising. Unlike ETL, the ELT architecture doesn’t require you
to decide on data types and formats in advance. In large-scale projects, two types of data
pipelines are often combined to enable both traditional and real-time analytics. Also, two
architectures can be involved to support Big Data analytics.

USE CASES OF DATA PIPELINES

As big data continues to grow, data management becomes an ever-increasing priority. While
data pipelines serve various functions, the following are for business applications:

 Exploratory data analysis: Data scientists use exploratory data analysis (EDA) to
analyze and investigate data sets and summarize their main characteristics, often
employing data visualization methods. It helps determine how best to manipulate data
sources to get the needed answers, making it easier for data scientists to discover
patterns, spot anomalies, test a hypothesis or check assumptions.
 Data visualizations: To represent data via common graphics, data visualizations such
as charts, plots, infographics, and even animations can be created. These visual
displays of information communicate complex data relationships and data-driven
insights in a way that is easy to understand.
  Machine learning: A branch of artificial intelligence (AI) and computer science,
machine learning focuses on the use of data and algorithms to imitate the way that
humans learn, gradually improving its accuracy. Through the use of statistical
methods, algorithms are trained to make classifications or predictions, uncovering key
insights within data mining projects.
 Data observability: To verify the accuracy and safety of the data being used, data
observability applies a variety of tools for monitoring, tracking and alerting for both
expected events and anomalies.

DATA ENGINEERING AND DATA WAREHOUSE

Data engineering is like designing and building the systems and infrastructure to manage
data. It’s the hero businesses need to ensure a reliable and secure system that can efficiently
process large amounts of data.

Data warehousing is like storing and managing large amounts of data for analysis and
reporting. It helps organizations make informed decisions and identify patterns and trends
that would otherwise be difficult to detect.

A data warehouse engineer manages the entire back-end development life cycle for the
company's data warehouse. The implementation of ETL procedures, cube building for
database and performance management, and dimensional design of the table structure are all
tasks that fall under the purview of data warehouse engineers.

A data warehouse engineer collaborates closely with the product management, senior data
engineering, data analysts, and data scientists teams to achieve insights, provide the
organization with valuable data solutions, and enable reliably informed strategic decisions.

What Does a Data Warehouse Engineer Do?

● Planning / Strategy

The Data Warehouse Engineer builds and supports the database and table schemas for both
new and existing data sources for the company’s data warehouse. Additionally, he develops
and maintains the ETL to make it easier for SSIS and other technologies to integrate data into
the warehouse. Data warehouse engineers create and design the technologies that keep the
company's data warehouse, ETL procedures, and business intelligence up to date.

● Data Analytics

The Data Warehouse Engineer performs an analytical role by analyzing business

requirements for monitoring analysis efficiently and accurately, turning relevant insights into
effective technical data architectures. The Data Warehouse Engineer establishes, creates, and
maintains technical specification documents for all reports and procedures.

● Collaborate with Other Teams

The data warehouse engineer collaborates closely with teams from many business
departments and disciplines to develop basic, practical, and effective solutions that meet the
data requirements of the entire organization. The Data Warehouse Engineer collaborates with
senior data analytics management and senior data warehouse engineering to streamline the
business's data needs.

ROLE OF DATA ENGINEER

Data engineers work in various settings to build systems that collect, manage, and convert
raw data into usable information for data scientists and business analysts to interpret. Their
ultimate goal is to make data accessible so that organizations can use it to evaluate and
optimize their performance.

DATA ENGINEERING PROCESS

The data engineering process covers a sequence of tasks that turn a large amount of raw data
into a practical product meeting the needs of analysts, data scientists, machine learning
engineers, and others. Typically, the end-to-end workflow consists of the following stages.
A data engineering process in brief.

Data ingestion (acquisition) moves data from multiple sources — SQL and NoSQL
databases, IoT devices, websites, streaming services, etc. — to a target system to be
transformed for further analysis. Data comes in various forms and can be both structured and
unstructured.

Data transformation adjusts disparate data to the needs of end users. It involves removing
errors and duplicates from data, normalizing it, and converting it into the needed format.

Data serving delivers transformed data to end users — a BI platform, dashboard, or data
science team.

BUILDING A DATA WAREHOUSE ARCHITECTURE

A Data warehouse is a heterogeneous collection of different data sources organized under a

unified schema. Builders should take a broad view of the anticipated use of the warehouse
while constructing a data warehouse. During the design phase, there is no way to anticipate
all possible queries or analyses. Some characteristic of Data warehouse are:

● Subject oriented

● Integrated

● Time Variant

● Non-volatile

Building a Data Warehouse – Some steps that are needed for building any data warehouse are
as following below:

1. To extract the data (transnational) from different data sources: For building a data
warehouse, data is extracted from various data sources and that data is stored in a central
storage area. For extraction of the data Microsoft has come up with an excellent tool. When
you purchase Microsoft SQL Server, then this tool will be available at free of cost.

2. To transform the transnational data: There are various DBMS where many of the
companies store their data. Some of them are: MS Access, MS SQL Server, Oracle, Sybase
etc. Also these companies save the data in spreadsheets, flat files, mail systems etc. Relating
data from all these sources is done while building a data warehouse.

3. To load the data (transformed) into the dimensional database: After building a
dimensional model, the data is loaded in the dimensional database. This process combines the
several columns together or it may split one field into the several columns. There are two
stages at which transformation of the data can be performed and they are: while loading the
data into the dimensional model or while data extraction from their origins.

4. To purchase a front-end reporting tool: There are top notch analytical tools available
in the market. These tools are provided by several major vendors. A cost effective tool and
Data Analyzer is released by Microsoft on its own.

For the warehouse there is an acquisition of the data. There must be a use of multiple and
heterogeneous sources for the data extraction, example databases. There is a need for the
consistency for which formation of data must be done within the warehouse. Reconciliation
of names, meanings and domains of data must be done from unrelated sources. There is also a
need for the installation of the data from various sources in the data model of the warehouse.
Conversion of the data might be done from object oriented, relational or legacy databases to a
multidimensional model. One of the largest labor demanding components of data warehouse
construction is data cleaning, which is one of the complex processes. Before loading of the
data in the warehouse, there should be cleaning of the data. All the work of loading must be
done in the warehouse for better performance. The only feasible and better approach for it is
incremental updating. Data storage in the data warehouse:

● Refresh the data

● To provide the time variant data

● To store the data as per the data model of the warehouse

● Purging the data

● To support the updating of the warehouse data

DATA WAREHOUSE

A data warehouse (DW) is a central repository storing data in queryable forms. From a
technical standpoint, a data warehouse is a relational database optimized for reading,
aggregating, and querying large volumes of data. Traditionally, DWs only contained
structured data or data that can be arranged in tables. However, modern DWs can also
support unstructured data (such as images, pdf files, and audio formats).

Without DWs, data scientists would have to pull data straight from the production database
and may report different results to the same question or cause delays and even outages.
Serving as an enterprise’s single source of truth, the data warehouse simplifies the
organization’s reporting and analysis, decision-making, and metrics forecasting.

Surprisingly, DW isn’t a regular database. How so?

First of all, they differ in terms of data structure. A typical database normalizes data
excluding any data redundancies and separating related data into tables. This takes up a lot of
computing resources, as a single query combines data from many tables. Contrarily, a DW
uses simple queries with few tables to improve performance and analytics.

Second, aimed at day-to-day transactions, standard transactional databases don’t usually store
historical data, while for warehouses, it’s their primary purpose, as they collect data from
multiple periods. DW simplifies a data analyst’s job, allowing for manipulating all data from
a single interface and deriving analytics, visualizations, and statistics.

Typically, a data warehouse doesn’t support as many concurrent users as a database designed
for a small group of analysts and decision-makers.
 DATA ARCHITECTURE WITH A DATA WAREHOUSE.
 To construct a data warehouse, four essential components are combined.
 Data warehouse storage. The foundation of data warehouse architecture is a database
that stores all enterprise data allowing business users to access it to draw valuable
insights.
 Data architects usually decide between on-premises and cloud-hosted DWs noting
how the business can benefit from this or that solution. Although the cloud
environment is more cost-efficient, easier to scale up or down, and isn’t limited to a
prescribed structure, it may lose to on-prem solutions regarding querying speed and
security. We’re going to list the most popular tools further on.
 A data architect can also design collective storage for your data warehouse – multiple
databases running in parallel. This will improve the warehouse’s scalability.
 Metadata. Adding business context to data, metadata helps transform it into
comprehensible knowledge. Metadata defines how data can be changed and
processed. It contains information about any transformations or operations applied to
source data while loading it into the data warehouse.
 Data warehouse access tools. These instruments vary in functionality. For example,
query and reporting tools are used for generating business analysis reports. And data
mining tools automate finding patterns and correlations in large amounts of data based
on advanced statistical modeling techniques.
 Data warehouse management tools. Spanning the enterprise, the data warehouse deals
with a number of management and administrative operations. Dedicated data
warehouse management tools exist to accomplish this.
 Data warehouses are a significant step forward in enhancing your data architecture.
However, DWs can be too bulky and slow to operate if you have hundreds of users
from different departments. In this case, data marts can be built and implemented to
increase speed and efficiency.
Big data engineering

Speaking about data engineering, we can’t ignore Big Data. Grounded in the four Vs –
volume, velocity, variety, and veracity – it usually floods large technology companies like
YouTube, Amazon, or Instagram. Big Data engineering is about building massive reservoirs
and highly scalable and fault-tolerant distributed systems.

Big data architecture differs from conventional data handling, as here we’re talking about
such massive volumes of rapidly changing information streams that a data warehouse can’t
accommodate. That’s where a data lake comes in handy.

Data lake

A data lake is a vast pool for saving data in its native, unprocessed form. It stands out for its
high agility as it isn’t limited to a warehouse’s fixed configuration.

Big data architecture with a data lake.

A data lake uses the ELT approach and starts data loading immediately after extracting it,
handling raw — often unstructured — data.

A data lake is worth building in those projects that will scale and need a more advanced
architecture. Besides, it’s very convenient when the purpose of the data hasn’t been
determined yet. In this case, you can load data quickly, store it, and modify it as necessary.

Data lakes are also a powerful tool for data scientists and ML engineers, who would use raw
data to prepare it for predictive analytics and machine learning.

Lakes are built on large, distributed clusters that would be able to store and process masses of
data. A famous example of such a data lake platform is Hadoop.
HADOOP AND ITS ECOSYSTEM

Hadoop is a large-scale, Java-based data processing framework capable of analyzing massive

datasets. The platform facilitates splitting data analysis jobs across various servers and
running them in parallel. It consists of three components:

Hadoop Distributed File System (HDFS) capable of storing Big Data,

a processing engine MapReduce, and

a resource manager YARN to control and monitor workloads.

Also, Hadoop benefits from a vast ecosystem of open-source tools that enhance its
capabilities and address various challenges of Big Data.

Hadoop ecosystem evolution.

Some popular instruments within the Hadoop ecosystem are

HBase, a NoSQL database built on top of HDFS that provides real-time access to read
or write data;

Apache Pig, Apache Hive, Apache Drill, and Apache Phoenix to simplify Big Data
exploration and analysis when working with HBase, HDFS, and MapReduce; and

Apache Zookeeper and Apache Oozie to coordinate operations and schedule jobs
across a Hadoop cluster.

Streaming analytics instruments

Tools enabling streaming analytics form a vital group within the Hadoop ecosystem. These
include
 Apache Spark, a computing engine for large datasets with near-real-time processing
capabilities;

Apache Storm, a real-time computing system for unbounded streams of data (those
that have a start but no defined end and must be continuously processed);

Apache Flink processing both unbounded and bounded data streams (those with a
defined start and end); and

Apache Kafka, a streaming platform for messaging, storing, processing, and

integrating large volumes of data.

Enterprise data hub

When a big data pipeline is not managed correctly, data lakes quickly become data swamps –
a collection of miscellaneous data that is neither governable nor usable. A new data
integration approach called a data hub emerged to tackle this problem.

Enterprise data hubs (EDHs) are the next generation of data architecture aiming at sharing
managed data between systems. They connect multiple sources of information, including
DWs and data lakes. Unlike DWs, the data hub supports all types of data and easily integrates
systems. Besides that, it can be deployed within weeks or even days while DW deployment
can last months and even years.

At the same time, data hubs come with additional capabilities for data management,
harmonizing, exploration, and analysis — something data lakes lack. They are business-
oriented and tailored for the most urgent organization’s needs.

To sum it all up,

a data warehouse is constructed to deal mainly with structured data for the purpose of
self-service analytics and BI;
 a data lake is built to deal with sizable aggregates of both structured and unstructured
data to support deep learning, machine learning, and AI in general; and

a data hub is created for multi-structured data portability, easier exchange, and
efficient processing.

An EDH can be integrated with a DW and/or a data lake to streamline data processing and
deal with these architectures' everyday challenges.

Role of data engineer

Juan De Dios Santos, a data engineer himself, defines this role in the following way: “In a
multidisciplinary team that includes data scientists, BI engineers, and data engineers, the role
of the data engineer is mostly to ensure the quality and availability of the data.” He also adds
that a data engineer might collaborate with others when implementing or designing a data-
related feature (or product) such as an A/B test, deploying a machine learning model, and
refining an existing data source.

Skills and qualifications

Data engineering lies at the intersection of software engineering and data science, which
leads to skill overlapping.

Software engineering background. Data engineers use programming languages to enable

reliable and convenient access to data and databases. Juan points out their ability to work
with the complete software development cycle, including ideation, architecture design,
prototyping, testing, deployment, DevOps, defining metrics, and monitoring systems. Data
engineers are experienced programmers in at least Python or Scala/Java.

Data-related skills. “A data engineer should have knowledge of multiple kinds of databases
(SQL and NoSQL), data platforms, concepts such as MapReduce, batch and stream
processing, and even some basic theory of data itself, e.g., data types, and descriptive
statistics,” underlines Juan.

Systems creation skills. Data engineers need experience with various data storage
technologies and frameworks they can combine to build data pipelines.

Toolkit

A data engineer should have a deep understanding of many data technologies to be able to
choose the right ones for a specific job.

Airflow. This Python-based workflow management system was developed by Airbnb to re-
architect its data pipelines. Migrating to Airflow, the company reduced their experimentation
reporting framework (ERF) run-time from 24+ hours to about 45 minutes. Among the
Airflow’s pros, Juan highlights its operators: “They allow us to execute bash commands, run
a SQL query or even send an email.” Juan also stresses Airflow’s ability to send Slack
notifications, complete and rich UI, and the overall maturity of the project. On the contrary,
Juan dislikes that Airflow only allows for writing jobs in Python.

Cloud Dataflow. A cloud-based data processing service, Dataflow is aimed at large-scale data
ingestion and low-latency processing through fast parallel execution of the analytics
pipelines. Dataflow is beneficial over Airflow as it supports multiple languages like Java,
Python, SQL, and engines like Flink and Spark. It is also well maintained by Google Cloud.
However, Juan warns that Dataflow’s high cost might be a disadvantage.

Other popular instruments are the Stitch platform for rapidly moving data and Blendo, a tool
for syncing various data sources with a data warehouse.

Warehouse solutions. Widely used on-premise data warehouse tools include Teradata Data
Warehouse, SAP Data Warehouse, IBM db2, and Oracle Exadata. The most popular cloud-
based data warehouse solutions are Amazon Redshift and Google BigQuery. Big data tools.
Technologies that a data engineer should master (or at least know of) are Hadoop and its
ecosystem, Elastic Stack for end-to-end big data analytics, data lakes, and more.
Choosing ETL Frameworks

There are two different paradigm options that you can choose from for your ETL: JVM-
centric and SQL-centric. JVM-centric ETLs are built in languages like Java and Scala.
Engineers prefer this option because it involves managing data transformation in a more
imperative manner.

SQL-centric ETLs are defined in more declarative ways than JVM models. They are centered
around SQL and tables, and data scientists like this model because it’s much easier to learn
than Java and Scala. That allows you to focus your time on the actual data rather than the
computer language to understand the data.

ETL and SQL: How They Work Together

In today's data-driven world, organizations are inundated with vast amounts of data generated
from various sources. However, it can be overwhelming and challenging to make sense of
data in its raw form. The real value of data lies in transforming it into actionable insights that
can drive business decisions and strategies. Most organizations that can effectively translate
data into insights gain a significant competitive advantage.

Although ETL (Extract, Transform, Load) and SQL (Structured Query Language) may
sometimes be seen as competing data processing methods, they can actually complement
each other. In fact, you often need SQL to get effective results from ETL.

By utilizing the strengths of each approach, organizations can optimize their data operations
by leveraging best practices accumulated over the years with the ETL method. Additionally,
by capitalizing on the flexibility and widespread use of SQL, businesses can effectively
transform complex data into meaningful and actionable insights.

What is ETL and what are the most common ETL tools?

ETL stands for Extract, Transform, Load. It is a process used in data integration to extract
data from various sources, transform the data to meet specific business requirements, and
load the transformed data into a target system, such as a data warehouse or a database. This
data is then used to inform decisions and answer business questions, often with business
Intelligence (BI) reports.

The extraction phase involves retrieving data from various sources, such as databases, flat
files, web services, or cloud-based applications. The transformation phase involves cleaning,
enriching, aggregating, or otherwise modifying the data to meet the needs of the target
system. Finally, the load phase involves writing the transformed data to the target system.

With the increasing adoption of big data technologies, such as Hadoop and Spark, ETL
processes have become more complex and require more advanced tools and technologies.
ETL workflows in big data often involve processing data in parallel across multiple nodes in
a distributed environment, which requires specialized tools that can handle data partitioning,
data shuffling, and fault tolerance.

There are many ETL pipelines and task automation tools available, ranging from open-source
solutions to commercial products. Some of the most common ETL tools include:

● Apache NiFi: is an open-source data integration tool that enables users to automate
the flow of data between systems. NiFi uses a visual data flow model, where data is
represented as "data flows" that move through a series of processors, each of which performs
a specific operation on the data.

● Apache Airflow: is an open-source platform for programmatically authoring,

scheduling, and monitoring workflows. It provides a way to create, execute, and manage
complex data pipelines that integrate data from multiple sources and systems. Airflow uses
Python scripts to define tasks and dependencies in a workflow, which are organized into a
directed acyclic graph (DAG) where each step would represent a specific data engineering
task.

● Microsoft SQL Server Integration Services (SSIS): SSIS is a data integration and ETL
platform introduced with SQL Server 2005 and is used for on-premises SQL Server
deployments. In 2015, Azure Data Factory (ADF) was introduced as a cloud-based no-code
data integration service to meet the increasing demand for cloud-based data processing.
● Informatica PowerCenter: Informatica PowerCenter is a comprehensive ETL tool that
provides a platform for designing, developing, and deploying data integration workflows. It
supports the extraction, transformation, and loading of data from various sources, including
databases, files, and cloud-based applications.

● Google Cloud Dataflow: Google Cloud Dataflow is a fully managed, cloud-based data
processing service for batch and streaming data. It is built on Apache Beam, an open-source
unified programming model for defining and executing data processing pipelines. With Cloud
Dataflow, users can develop and execute data processing pipelines in a fully managed and
optimized environment, without the need for infrastructure management.

● AWS Glue: AWS Glue is a fully managed, serverless ETL (Extract, Transform, Load)
service provided by Amazon Web Services (AWS). It is designed to make it easy for users to
extract data from a variety of sources, transform it, and then load data into data stores for
analysis. AWS Glue automates the process of building ETL workflows, including data
schema discovery, data transformation, and job scheduling.

SQL CENTRIC ETL

What is SQL?

SQL (Structured Query Language) is a domain-specific language that is used to manage and
manipulate data within relational database management systems (RDBMS). It is designed to
be declarative, meaning that users specify what they want the database to do, rather than how
to do it.

There are several standard SQL commands that can be used to interact with a database. Here
are some of the most common ones:

● SELECT: retrieve data from a table or view

● INSERT: insert data into a table

● UPDATE: update existing data in a table

● DELETE: delete data from a table

● CREATE: create a new table, view, or other database object

● ALTER: modify an existing table, view, or other database object

● DROP: delete an existing table, view, or other database object

● TRUNCATE: delete all data from a table, but keep the structure intact

These commands are often combined in various ways to create more complex SQL
statements, such as JOINs, subqueries, and aggregate functions.

Here is an example of a simple SQL query:

Select * from customers WHERE state= ‘California’.

This query selects all columns from the customers table where the state column has a value of
'California'.

It's important to note that while SQL includes these standard commands, different database
management systems may have proprietary extensions or additional features specific to their
platform, examples of this are:

● Microsoft SQL Server: T-SQL (Transact-SQL) is a proprietary extension to SQL used

in Microsoft SQL Server. It includes additional functions, operators, and programming
constructs that are not part of standard SQL.

● Oracle Database: PL/SQL (Procedural Language/Structured Query Language) is a

proprietary extension to SQL used in Oracle Database. It includes programming constructs,
such as loops, conditions, and variables, that allow developers to write complex procedures
and functions.
● IBM Db2: SQL PL is a proprietary extension to SQL used in IBM Db2. It includes
procedural language constructs, such as loops and conditionals, that allow developers to write
complex database procedures and functions.

● PostgreSQL: PL/pgSQL is a proprietary extension to SQL used in PostgreSQL. It

includes programming constructs, such as variables and loops, that allow developers to write
complex database procedures and functions.

● MySQL: MySQL offers several proprietary extensions to SQL, such as the

GROUP_CONCAT function, which concatenates values from multiple rows into a single
string, and the INSERT ... ON DUPLICATE KEY UPDATE statement, which updates a row
if it already exists, or inserts a new row if it does not.

SQL queries in ETL

ETL and SQL are often used together in data warehousing systems. ETL processes extract
data from different sources, transforms it, and loads it into a data warehouse where it can be
used for reporting and analysis. SQL commands are used to perform actions on selected
tables and rows of data in the data warehouse, known as a SQL query.

In ETL processes, data is often transformed using complex SQL queries to ensure that it is
accurate and consistent. This involves extracting data from various data sources, and
transforming it to meet the needs of the data warehouse. For example, data may need to be
cleaned, filtered, or aggregated before it can be loaded into the data warehouse.

SQL is also used to retrieve data from the data warehouse for analysis and reporting. Data
analysts and business users can use SQL queries to extract specific data from the warehouse
and create custom reports and visualizations. SQL commands such as SELECT, FROM,
WHERE, GROUP BY, and ORDER BY are commonly used in these queries.

Let's look at an example where you may have two databases: one with customer information
and one with order information. You want to combine these two databases into a data
warehouse to analyze customer behavior and purchase patterns.
First, you would use ETL to extract the relevant data from each database, transform it into a
common format, and load data into the data warehouse.

Next, you could use a SQL query to join the two tables in the data warehouse based on a
shared customer ID field. The SQL query might look something like this:

Copy

SELECT customers.name, orders.order_date, orders.total_amount

FROM customers

INNER JOIN orders ON customers.customer_id = orders.customer_id;

This query would select the customer's name, order date, and total amount for each order,
joining the customer and order tables based on the customer ID field. You could then use this
data to analyze customer behavior, identify popular products, or make targeted marketing
decisions.

SQL queries in different ETL testing categories

ETL testing categories are the different types or stages of testing that are involved in the
process of ETL testing. These categories are designed to ensure that the ETL process is
working as intended and that data is being extracted, transformed, and loaded correctly.

1. Metadata testing: This involves checking whether the metadata of the source and
target systems are in sync, and that the data types, lengths, and formats of the data are
consistent. SQL queries can be used to verify metadata consistency between the source and
target systems.

2. Data quality testing: This type of testing involves checking the accuracy,
completeness, and consistency of data after it has been transformed. SQL queries can be used
to check data quality by comparing source data to target data after it has been transformed.
3. Data completeness testing: This involves verifying that all data from the source
system has been successfully loaded into the target system. SQL queries can be used to check
data completeness by comparing the number of records in the source and target systems.

4. ETL performance testing: This type of testing involves measuring the performance of
the ETL process and identifying any bottlenecks or areas of improvement. SQL queries can
be used to measure ETL performance by analyzing query execution times and identifying any
slow queries.

5. Data transformation testing: This involves verifying that data has been transformed
according to the business rules and transformation logic specified in the ETL process. SQL
queries can be used to verify data transformation by checking whether the transformed data
meets the specified business rules.

6. ETL data integration testing: This type of testing involves verifying that the ETL
process is integrated with other systems and applications in the organization. SQL queries can
be used to verify ETL integration by checking data consistency between the ETL system and
other systems.

SQL plays a crucial role in ETL processes. It enables data analysts and developers to extract
data from various sources, transform and manipulate it to fit the target system's schema, and
then load it into the destination database or data warehouse, giving way for various advanced
use cases such as Machine learning and AI applications.

SQL's ability to handle complex data transformations and queries makes it an essential tool
for ETL operations.

While there are many ETL tools available in the market that have SQL built-in to their
platform, having a solid understanding of SQL is still fundamental to work with ETL
processes and optimize data integration workflows.

With the ever-increasing amount of data that needs to be processed and analyzed, SQL will
continue to play a critical role in ETL operations in the future.
ETL-java centric

Some ETL Tools with Java

The Java platform is a free software download that many of today’s websites and apps can’t
run without. Java is practically a requirement for most internal and cloud applications.

Developers use its object-oriented programming language to build desktop and mobile apps.
You can write complex ETL (extract, transform and load) processes in Java that go beyond
what’s available out of the box in most ETL tools.

If you use Java to script code for data transformations or other ETL functions, you also need
an ETL tool that supports Java work. Java is one of the most popular and powerful scripting
languages. And there’s an abundance of open source and paid ETLs to choose from that work
with Java code. You won’t have any trouble finding one that meets your specific data project
needs.

The study here gives you information on some of the best open source ETLs for Java. Some
ETLs that used to be open source have become paid services. At the end of the blog, we also
list some paid ETLs that might meet your needs for big BI data projects that need pro-level
support. Free and open source Java ETLs

Apache Airflow, Talend, and Spring Batch are all popular Java-based ETL frameworks that
offer powerful tools. The frameworks offer powerful tools to help streamline your workflow:
Apache Airflow helps orchestrate workflows by scheduling tasks dependent on one another.

Introduction to Java for ETL

With its advanced database connectivity features, you can easily connect to your data sources
and gain access to valuable information. Plus, there are some great visualization tools
available that allow you to effectively work with large datasets. These tools allow you to
validate data in real time.

Nowadays, many organizations use Java for developing complex systems due to its
scalability. Understanding this language is essential if you want to stay ahead in the game.

Java-Based ETL Frameworks: Apache Airflow, Talend, Spring Batch

Think of moving data around like driving a car — you have to know the ins and outs before
taking off. The same is true for ETL with Java. It’s important to understand the fundamentals
first; then, you can start exploring different frameworks.

Apache Airflow, Talend, and Spring Batch are all popular Java-based ETL frameworks that
offer powerful tools. The frameworks offer powerful tools to help streamline your workflow:

● Apache Airflow helps orchestrate workflows by scheduling tasks dependent on one

another. It also integrates easily with BigQuery API, so you can quickly build up complex
data pipelines without having to reinvent the wheel each time.
● Talend Studio makes managing big data simple through visual drag-and-drop
components that enable quick development cycles from ingestion to analysis. Plus, it has
powerful integration built into Apache Spark, allowing machine learning models to be
deployed more efficiently.

● Finally, Spring Batch offers an extensible framework for processing large volumes of
data in batches – while providing full control over reading/writing resources and transactions,
which allows developers greater flexibility when designing their ETL processes.

These three options provide developers with plenty of choice depending on their particular
needs, ensuring they’re able to get the most out of their projects without sacrificing quality or
efficiency.

Java with ETL

ETL Workflows Using Java: Data Extraction, Transformation, And Loading

As an ETL developer with experience in Java, one can also perform tasks in data extraction,
transformation, and loading workflows. When it comes to this crucial process of managing
data sets between two systems, there are several key considerations that must be taken into
account.

First is the need for careful data cleansing prior to mapping out a database which can then
facilitate the efficient transfer of information.
Automation tools such as Apache Airflow can provide additional support here by scheduling
tasks according to user requirements.

Exception handling should also be considered as part of any larger workflow system in order
to ensure smooth operation when errors occur.

Finally, performance tuning is paramount for ensuring optimal results from your ETL
pipeline – something that needs to be continuously monitored throughout its life cycle.

By applying these best practices early on during development, you’ll avoid costly delays or
problems down the line as you manage greater volumes of data over time.

1. Apache Spark

Spark has become a popular addition to ETL workflows. The Spark quickstart shows you
how to write a self-contained app in Java. You can get even more functionality with one of
Spark’s many Java API packages.

Spark has all sorts of data processing and transformation tools built in. It’s designed to run
computations in parallel, so even large data jobs run fast—100 times faster than Hadoop,
according to the Spark website. And it scales up for big data operations and can run
algorithms in stream. Spark has tools for fast data streaming, machine learning and graph
processing that output to storage or live dashboards.
Spark is supported by the community. If you need help, try its mailing lists, in-person groups
and issue tracker.

2. Jaspersoft ETL

Jaspersoft ETL is a free platform that works with Java. With this open source ETL tool, you
can embed dynamic reports and print-quality files into your Java apps and websites. It
extracts report data from any data source and exports to 10 formats.

If you’re a developer, Jaspersoft ETL is an easy-to-use choice for data integration projects.
You can download the community edition for free. The open source version is recommended
for small work groups. For larger enterprises and professional-level support, you might opt
for the enterprise edition.

3. Scriptella

Scriptella is an open source ETL tool that was written in Java. It was created for
programmers to simplify data transformation work. To embed or invoke Java code in
Scriptella, you need the Janino or JavaScript bridge driver or the Service Provider Interface
(SPI). The SPI is a Scriptella API plug-in that’s a bit more complicated. See the Using Java
Code section in the Scriptella documentation for more options on using Java in Scriptella.

Scriptella supports cross-database ETL scripts, and it works with multiple data sources in a
single ETL file. This ETL tool is a good choice to use with Java when you’ve got source data
in different database formats that needs to be run in a combined transformation. For more
info ETL Training

4. Apatar

If you work with CRM systems, Apatar, a Java-based open source ETL tool, might be a good
choice. It moves and synchronizes customer data between your own systems and third-party
applications. Apatar can transform and integrate large, complex customer datasets. You can
customize this free tool with the Java source code that’s included in the package.
The Apatar download saves time and resources by leveraging built-in app integration tools
and reusing mapping schemas that you create. Even non-developers can work with Apatar’s
user-friendly drag-and-drop UI. No programming, design or coding is required with this cost-
saving, but powerful, data migration tool that makes CRM work easier.

5. Pentaho Kettle

Pentaho’s Data Integration (PDI), or Kettle (Kettle E.T.T.L. Environment), is an open source
ETL tool that uses Pentaho’s own metadata-based integration method. Kettle documentation
includes Java API examples.

With Kettle, you can move and transform data, create and run jobs, load balance data, pull
data from multiple sources, and more. But you can’t sequence your transformations. You’ll
need Spoon, the GUI for designing jobs and transformations that work with Kettle’s tools:
Pan does data transformation, and Kitchen runs your jobs. However, Spoon has some
reported issues. Learn more from ETL Testing Course

6. Talend Open Source Data Integrator

Go past basic data analysis and storage with Talend Open Studio for Data Integration, a
cloud-friendly ETL tool that can embed Java code libraries. Open Studio’s robust toolbox lets
you work with code, manage files, and transform and integrate big data. It gives you
graphical design and development tools and hundreds of data processing components and
connectors.

With Talend’s Open Studio, you can import external code, create and expand your own, and
view and test it in a runtime environment. Check your final products with Open Studio’s Data
Quality & Profiling and Data Preparation features.

7. Spring Batch

Spring Batch is a full-service ETL tool that is heavy on documentation and training
resources. This lightweight, easy-to-use tool delivers robust ETL for batch applications. With
Spring Batch, you can build batch apps, process small or complex batch jobs, and scale up for
high-volume data processing. It has reusable functions and advanced technical features like
transaction management, chunk-based processing, web-based admin interface and more. For
more skills ETL Testing Online Training

8. Easy Batch

The Easy Batch framework uses Java to make batch processing easier. This open source ETL
tool reads, filters and maps your source data in sequence. It processes your job in a pipeline,
writes your output in batches to your data warehouse, and gives you a job report. With Easy
Batch’s APIs, you can process different source data types consistently. The Easy Batch ETL
tool transforms your Java code into usable data for reporting, testing and analysis.

9. Apache Camel

Apache Camel is an open source Java framework that integrates different apps by using
multiple protocols and technologies. It’s a small ETL library with only one API for you to
learn. To configure routing and mediation rules, Apache Camel provides Java object-based
implementation of Enterprise Integration Patterns (EIPs) using an API or declarative Java
domain-specific language. EIPs are design patterns that enable enterprise application
integration and message-oriented middleware.

Apache Camel uses Uniform Resource Identifiers (URIs), a naming scheme that refers to an
endpoint that provides information. Examples are what components are used, the context path
and the options applied against the component. This ETL tool has more than 100
components, including FTP, JMX and HTTP. It runs as a standalone application in a web
container like Apache Tomcat, a JEEE application server like WildFly, or combined with a
Spring container.

10. Bender

Amazon’s AWS Lambda runs serverless code and does basic ETL, but you might need
something more. Bender is a Java-based framework designed to build ETL modules in
Lambda. For example, this open source ETL appends GeoIP info to your log data, so you can
create data-driven geological dashboards in Kibana. Out of the box, it reads, writes and
transforms input that supports Java code: Amazon Kinesis Streams and Amazon S3.

Bender is a robust, strongly documented and supported ETL tool that enhances your data
operations. It gives you multiple operations, handlers, deserializers and serializers,
transporters and reporters that go beyond what’s available in Lambda.

Streamline your ETL Process with Sourcetable

Sourcetable simplifies the ETL process by automatically syncing your live Java data from a
variety of apps or databases.

Extract, Transform, Load (ETL) processes are crucial for managing and interpreting the vast
amounts of data in today's digital landscape, especially when leveraging the robust
capabilities of Java. Java's advanced features for data modeling, object mapping, and
database connectivity, combined with its scalability, make it an invaluable language for ETL
operations. Whether for real-time data aggregation or building reliable systems for
demanding workloads, Java-based ETL tools such as Apache Airflow, Talend, and Spring
Batch streamline workflows, simplifying the management of large datasets and enhancing the
overall efficiency. ETL with Java is particularly valuable when loading data into
spreadsheets, where the visualization tools can validate data in real time, allowing for
immediate insights and decision-making. On this page, we'll delve into the essence of Java,
explore various ETL tools tailored for Java data, examine use cases that demonstrate the
versatility of ETL with Java, introduce Sourcetable as an alternative approach to ETL, and
address common questions surrounding ETL with Java data. Our goal is to provide a
comprehensive educational resource for anyone looking to harness the power of Java for ETL
processes.

What is Java?

Java is a versatile and powerful software tool that includes a suite of utilities enabling the
development and management of applications and services. Among its core tools is the javac
tool, which compiles Java code into bytecode, an intermediate language that Java Virtual
Machine (JVM) interprets. This makes Java applications platform-independent, capable of
running on any device with a JVM.

For monitoring and troubleshooting, Java offers tools like jvisualvm and jconsole, which
provide graphical interfaces for performance analysis and management of running Java
applications. Additional utilities such as jinfo, jps, jstack, jmap, and jstat furnish detailed
insights into the behavior of Java programs, aiding developers in observing and tuning their
applications.

Java also includes advanced tools for in-depth analysis, such as Java Mission Control and
Java Flight Recorder, which are instrumental for continuous data collection and incident
analysis. Furthermore, the apt tool supports the processing of annotations in Java code,
enhancing the development process.

On the services side, Java enables the creation of service objects—classes with public
methods that implement interfaces to expose significant processes. These objects are not tied
to a specific entity but provide general-purpose functionality within applications. Java SE
also encompasses tools for distributed computing and communication, such as those for
CORBA, RMI, and security management, reinforcing Java's role as a comprehensive
platform for building robust software services.

ETL Tools for Java

Java, known for its robust data modeling and object mapping capabilities, also offers
advanced features for database connectivity, making it a preferred language for developing
complex, scalable systems. There is an array of Java-based ETL (Extract, Transform, Load)
frameworks designed to facilitate data integration and management. Apache Airflow, Talend,
and Spring Batch are among the popular Java-based ETL frameworks. Apache Airflow
excels in orchestrating workflows and scheduling tasks, with notable integration with
BigQuery API. Talend Studio simplifies big data management and integrates seamlessly with
Apache Spark, while Spring Batch specializes in processing large volumes of data in batches,
giving users full control over resource interactions and transactions.
Other Java ETL frameworks include Data Pipeline, which is free and capable of handling
both batch and streaming data through a single API. Scriptella and Cascading offer open-
source solutions for ETL processes, with Cascading providing additional capabilities such as
custom pipes and taps, and support for complex data operations like sorting and filtering.
Furthermore, commercial ETL tools like ETLWorks and CloverDX offer trial periods and
feature extensive libraries for built-in transformations, allowing users to create custom
transformations and connectors. Oracle Data Integrator and Smooks present more specialized
options, with Oracle focusing on high-volume batch loads and Smooks on transforming and
binding data sources into Java Object Models.

Java users have a wide range of ETL tools at their disposal, extending beyond Java-specific
frameworks. These include cross-platform solutions such as Apache Airflow, Talend Open
Studio, Hadoop, and cloud-based services like AWS Glue, AWS Data Pipeline, Azure Data
Factory, and Google Cloud Dataflow. Each tool offers unique features catering to different
requirements, from batch processing to real-time data integration, and from simple data jobs
to complex business operations. The choice of an ETL tool for Java should be guided by the
specific needs of the project, taking into account factors like performance, scalability, and
integration capabilities.

Streamline Your ETL Processes with Sourcetable

For Java developers seeking an efficient way to manage ETL tasks, Sourcetable presents a
compelling solution. Unlike traditional third-party ETL tools or the complexities involved in
crafting a custom ETL framework, Sourcetable offers a seamless and automated approach to
extract-transform-load operations. With its ability to sync live data from a diverse range of
apps and databases, Sourcetable streamlines the integration process, allowing you to focus on
what matters most: analyzing and interpreting your data.

One of the key advantages of using Sourcetable is its spreadsheet-like interface, which is
intuitive for users at all technical levels. This familiar format significantly reduces the
learning curve, enabling teams to quickly leverage the data they need without the overhead of
mastering new software. Additionally, Sourcetable's emphasis on automation and business
intelligence means that routine data tasks can be set up to run with minimal intervention,
saving time and reducing the potential for human error.
By choosing Sourcetable for your ETL needs, you're not just simplifying the process of
loading data into a user-friendly interface; you're also empowering your organization with the
tools needed for effective decision-making. The convenience and efficiency provided by
Sourcetable can lead to enhanced productivity and deeper insights, making it a smart choice
for Java developers looking to optimize their data workflow.

Common Use Cases

Java ETL can be used to extract data from various sources and load it into a spreadsheet for
reporting and analysis

Java ETL can be used to cleanse, validate, and enrich data from Excel files before importing
it into a spreadsheet

Java ETL can be used to consolidate data from Outlook files and other sources into a single
spreadsheet to maintain a single version of truth.